ANCOVA in flow (math) (flow
(math))
Geiser C. Challco geiser@alumni.usp.br
NOTE:
- Teste ANCOVA para determinar se houve diferenças significativas no
flow (math) (medido usando pre- e pos-testes).
- ANCOVA test to determine whether there were significant differences
in flow (math) (measured using pre- and post-tests).
Setting Initial Variables
dv = "flow.math"
dv.pos = "fss.media.math"
dv.pre = "dfs.media.math"
fatores2 <- c("Sexo","Zona","Cor.Raca","Serie")
lfatores2 <- as.list(fatores2)
names(lfatores2) <- fatores2
fatores1 <- c("grupo", fatores2)
lfatores1 <- as.list(fatores1)
names(lfatores1) <- fatores1
lfatores <- c(lfatores1)
color <- list()
color[["prepost"]] = c("#ffee65","#f28e2B")
color[["grupo"]] = c("#bcbd22","#fd7f6f")
color[["Sexo"]] = c("#FF007F","#4D4DFF")
color[["Zona"]] = c("#AA00FF","#00CCCC")
color[["Cor.Raca"]] = c(
"Parda"="#b97100","Indígena"="#9F262F",
"Branca"="#87c498", "Preta"="#848283","Amarela"="#D6B91C"
)
level <- list()
level[["grupo"]] = c("Controle","Experimental")
level[["Sexo"]] = c("F","M")
level[["Zona"]] = c("Rural","Urbana")
level[["Cor.Raca"]] = c("Parda","Indígena","Branca", "Preta","Amarela")
level[["Serie"]] = c("6 ano","7 ano","8 ano","9 ano")
# ..
ymin <- 0
ymax <- 0
ymin.ci <- 0
ymax.ci <- 0
color[["grupo:Sexo"]] = c(
"Controle:F"="#ff99cb", "Controle:M"="#b7b7ff",
"Experimental:F"="#FF007F", "Experimental:M"="#4D4DFF",
"Controle.F"="#ff99cb", "Controle.M"="#b7b7ff",
"Experimental.F"="#FF007F", "Experimental.M"="#4D4DFF"
)
color[["grupo:Zona"]] = c(
"Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
"Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
"Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
"Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
color[["grupo:Cor.Raca"]] = c(
"Controle:Parda"="#e3c699", "Experimental:Parda"="#b97100",
"Controle:Indígena"="#e2bdc0", "Experimental:Indígena"="#9F262F",
"Controle:Branca"="#c0e8cb", "Experimental:Branca"="#87c498",
"Controle:Preta"="#dad9d9", "Experimental:Preta"="#848283",
"Controle:Amarela"="#eee3a4", "Experimental:Amarela"="#D6B91C",
"Controle.Parda"="#e3c699", "Experimental.Parda"="#b97100",
"Controle.Indígena"="#e2bdc0", "Experimental.Indígena"="#9F262F",
"Controle.Branca"="#c0e8cb", "Experimental.Branca"="#87c498",
"Controle.Preta"="#dad9d9", "Experimental.Preta"="#848283",
"Controle.Amarela"="#eee3a4", "Experimental.Amarela"="#D6B91C"
)
for (coln in c("vocab","vocab.teach","vocab.non.teach","score.tde",
"TFL.lidas.per.min","TFL.corretas.per.min","TFL.erradas.per.min","TFL.omitidas.per.min",
"leitura.compreensao")) {
color[[paste0(coln,".quintile")]] = c("#BF0040","#FF0000","#800080","#0000FF","#4000BF")
level[[paste0(coln,".quintile")]] = c("1st quintile","2nd quintile","3rd quintile","4th quintile","5th quintile")
color[[paste0("grupo:",coln,".quintile")]] = c(
"Experimental.1st quintile"="#BF0040", "Controle.1st quintile"="#d8668c",
"Experimental.2nd quintile"="#FF0000", "Controle.2nd quintile"="#ff7f7f",
"Experimental.3rd quintile"="#8fce00", "Controle.3rd quintile"="#ddf0b2",
"Experimental.4th quintile"="#0000FF", "Controle.4th quintile"="#b2b2ff",
"Experimental.5th quintile"="#4000BF", "Controle.5th quintile"="#b299e5",
"Experimental:1st quintile"="#BF0040", "Controle:1st quintile"="#d8668c",
"Experimental:2nd quintile"="#FF0000", "Controle:2nd quintile"="#ff7f7f",
"Experimental:3rd quintile"="#8fce00", "Controle:3rd quintile"="#ddf0b2",
"Experimental:4th quintile"="#0000FF", "Controle:4th quintile"="#b2b2ff",
"Experimental:5th quintile"="#4000BF", "Controle:5th quintile"="#b299e5")
}
gdat <- read_excel("../data/data.xlsx", sheet = "sumary")
gdat <- gdat[which(is.na(gdat$Necessidade.Deficiencia) & !is.na(gdat$Stari.Grupo)),]
dat <- gdat
dat$grupo <- factor(dat[["Stari.Grupo"]], level[["grupo"]])
for (coln in c(names(lfatores))) {
dat[[coln]] <- factor(dat[[coln]], level[[coln]][level[[coln]] %in% unique(dat[[coln]])])
}
dat <- dat[which(!is.na(dat[[dv.pre]]) & !is.na(dat[[dv.pos]])),]
dat <- dat[,c("id",names(lfatores),dv.pre,dv.pos)]
dat.long <- rbind(dat, dat)
dat.long$time <- c(rep("pre", nrow(dat)), rep("pos", nrow(dat)))
dat.long$time <- factor(dat.long$time, c("pre","pos"))
dat.long[[dv]] <- c(dat[[dv.pre]], dat[[dv.pos]])
for (f in c("grupo", names(lfatores))) {
if (is.null(color[[f]]) && length(unique(dat[[f]])) > 0)
color[[f]] <- distinctColorPalette(length(unique(dat[[f]])))
}
for (f in c(fatores2)) {
if (is.null(color[[paste0("grupo:",f)]]) && length(unique(dat[[f]])) > 0)
color[[paste0("grupo:",f)]] <- distinctColorPalette(length(unique(dat[["grupo"]]))*length(unique(dat[[f]])))
}
ldat <- list()
laov <- list()
lpwc <- list()
lemms <- list()
Descriptive Statistics
of Initial Data
df <- get.descriptives(dat, c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1)
get.descriptives(dat, c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
## There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
dfs.media.math |
99 |
3.290 |
3.222 |
2.333 |
4.444 |
0.487 |
0.049 |
0.097 |
0.556 |
YES |
0.309 |
-0.323 |
| Experimental |
|
|
|
|
dfs.media.math |
46 |
3.271 |
3.278 |
1.889 |
4.556 |
0.604 |
0.089 |
0.179 |
0.889 |
YES |
-0.118 |
-0.567 |
|
|
|
|
|
dfs.media.math |
145 |
3.284 |
3.222 |
1.889 |
4.556 |
0.525 |
0.044 |
0.086 |
0.778 |
YES |
0.100 |
-0.276 |
| Controle |
|
|
|
|
fss.media.math |
99 |
3.426 |
3.444 |
2.444 |
5.000 |
0.546 |
0.055 |
0.109 |
0.764 |
NO |
0.577 |
0.193 |
| Experimental |
|
|
|
|
fss.media.math |
46 |
3.284 |
3.333 |
2.000 |
4.556 |
0.674 |
0.099 |
0.200 |
0.861 |
YES |
0.183 |
-0.872 |
|
|
|
|
|
fss.media.math |
145 |
3.381 |
3.333 |
2.000 |
5.000 |
0.591 |
0.049 |
0.097 |
0.750 |
YES |
0.330 |
-0.146 |
| Controle |
F |
|
|
|
dfs.media.math |
45 |
3.310 |
3.333 |
2.444 |
4.444 |
0.464 |
0.069 |
0.139 |
0.667 |
YES |
0.293 |
-0.617 |
| Controle |
M |
|
|
|
dfs.media.math |
54 |
3.273 |
3.222 |
2.333 |
4.444 |
0.509 |
0.069 |
0.139 |
0.556 |
YES |
0.328 |
-0.260 |
| Experimental |
F |
|
|
|
dfs.media.math |
17 |
3.046 |
3.000 |
1.889 |
4.556 |
0.688 |
0.167 |
0.354 |
0.667 |
YES |
0.455 |
-0.463 |
| Experimental |
M |
|
|
|
dfs.media.math |
29 |
3.402 |
3.444 |
2.222 |
4.333 |
0.517 |
0.096 |
0.197 |
0.667 |
YES |
-0.360 |
-0.523 |
| Controle |
F |
|
|
|
fss.media.math |
45 |
3.389 |
3.333 |
2.444 |
4.556 |
0.507 |
0.076 |
0.152 |
0.667 |
YES |
0.401 |
-0.472 |
| Controle |
M |
|
|
|
fss.media.math |
54 |
3.456 |
3.444 |
2.444 |
5.000 |
0.580 |
0.079 |
0.158 |
0.771 |
NO |
0.625 |
0.273 |
| Experimental |
F |
|
|
|
fss.media.math |
17 |
3.218 |
3.333 |
2.000 |
4.556 |
0.777 |
0.188 |
0.399 |
1.000 |
YES |
0.157 |
-1.234 |
| Experimental |
M |
|
|
|
fss.media.math |
29 |
3.323 |
3.333 |
2.222 |
4.556 |
0.617 |
0.115 |
0.235 |
0.889 |
YES |
0.295 |
-0.878 |
| Controle |
|
Rural |
|
|
dfs.media.math |
54 |
3.274 |
3.111 |
2.333 |
4.444 |
0.553 |
0.075 |
0.151 |
0.719 |
YES |
0.404 |
-0.685 |
| Controle |
|
Urbana |
|
|
dfs.media.math |
17 |
3.222 |
3.222 |
2.333 |
4.000 |
0.480 |
0.116 |
0.247 |
0.667 |
YES |
-0.031 |
-1.067 |
| Controle |
|
|
|
|
dfs.media.math |
28 |
3.361 |
3.333 |
2.667 |
4.222 |
0.340 |
0.064 |
0.132 |
0.444 |
NO |
0.667 |
0.374 |
| Experimental |
|
Rural |
|
|
dfs.media.math |
29 |
3.360 |
3.333 |
2.222 |
4.556 |
0.524 |
0.097 |
0.199 |
0.556 |
YES |
0.262 |
-0.189 |
| Experimental |
|
Urbana |
|
|
dfs.media.math |
8 |
2.819 |
2.667 |
1.889 |
4.111 |
0.732 |
0.259 |
0.612 |
0.694 |
YES |
0.434 |
-1.220 |
| Experimental |
|
|
|
|
dfs.media.math |
9 |
3.383 |
3.667 |
2.444 |
4.111 |
0.614 |
0.205 |
0.472 |
1.111 |
YES |
-0.461 |
-1.699 |
| Controle |
|
Rural |
|
|
fss.media.math |
54 |
3.406 |
3.444 |
2.444 |
4.667 |
0.528 |
0.072 |
0.144 |
0.556 |
YES |
0.139 |
-0.317 |
| Controle |
|
Urbana |
|
|
fss.media.math |
17 |
3.611 |
3.444 |
2.667 |
5.000 |
0.749 |
0.182 |
0.385 |
1.222 |
NO |
0.645 |
-1.076 |
| Controle |
|
|
|
|
fss.media.math |
28 |
3.352 |
3.333 |
2.778 |
4.000 |
0.420 |
0.079 |
0.163 |
0.750 |
YES |
0.220 |
-1.413 |
| Experimental |
|
Rural |
|
|
fss.media.math |
29 |
3.280 |
3.333 |
2.000 |
4.556 |
0.653 |
0.121 |
0.248 |
0.778 |
YES |
0.093 |
-0.754 |
| Experimental |
|
Urbana |
|
|
fss.media.math |
8 |
3.184 |
2.889 |
2.444 |
4.333 |
0.735 |
0.260 |
0.615 |
0.840 |
NO |
0.610 |
-1.484 |
| Experimental |
|
|
|
|
fss.media.math |
9 |
3.387 |
3.333 |
2.111 |
4.556 |
0.752 |
0.251 |
0.578 |
1.111 |
YES |
-0.059 |
-1.213 |
| Controle |
|
|
Parda |
|
dfs.media.math |
46 |
3.346 |
3.333 |
2.333 |
4.444 |
0.552 |
0.081 |
0.164 |
0.750 |
YES |
0.106 |
-0.877 |
| Controle |
|
|
Branca |
|
dfs.media.math |
9 |
3.395 |
3.333 |
2.889 |
4.444 |
0.553 |
0.184 |
0.425 |
0.444 |
NO |
0.749 |
-1.034 |
| Controle |
|
|
Preta |
|
dfs.media.math |
1 |
2.889 |
2.889 |
2.889 |
2.889 |
|
|
|
0.000 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
dfs.media.math |
43 |
3.217 |
3.222 |
2.333 |
4.111 |
0.392 |
0.060 |
0.121 |
0.444 |
YES |
-0.020 |
-0.194 |
| Experimental |
|
|
Parda |
|
dfs.media.math |
12 |
3.315 |
3.333 |
2.444 |
4.222 |
0.540 |
0.156 |
0.343 |
0.750 |
YES |
0.097 |
-1.312 |
| Experimental |
|
|
Indígena |
|
dfs.media.math |
5 |
3.422 |
3.556 |
2.667 |
4.111 |
0.552 |
0.247 |
0.686 |
0.556 |
YES |
-0.140 |
-1.786 |
| Experimental |
|
|
Branca |
|
dfs.media.math |
6 |
3.093 |
3.333 |
2.222 |
3.667 |
0.568 |
0.232 |
0.596 |
0.667 |
NO |
-0.502 |
-1.742 |
| Experimental |
|
|
Amarela |
|
dfs.media.math |
1 |
3.778 |
3.778 |
3.778 |
3.778 |
|
|
|
0.000 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
dfs.media.math |
22 |
3.237 |
3.111 |
1.889 |
4.556 |
0.683 |
0.146 |
0.303 |
1.056 |
YES |
0.004 |
-0.728 |
| Controle |
|
|
Parda |
|
fss.media.math |
46 |
3.478 |
3.444 |
2.444 |
5.000 |
0.552 |
0.081 |
0.164 |
0.750 |
YES |
0.321 |
-0.031 |
| Controle |
|
|
Branca |
|
fss.media.math |
9 |
3.481 |
3.556 |
2.444 |
4.667 |
0.603 |
0.201 |
0.464 |
0.444 |
YES |
0.254 |
-0.347 |
| Controle |
|
|
Preta |
|
fss.media.math |
1 |
3.333 |
3.333 |
3.333 |
3.333 |
|
|
|
0.000 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
fss.media.math |
43 |
3.361 |
3.222 |
2.667 |
5.000 |
0.540 |
0.082 |
0.166 |
0.778 |
NO |
0.899 |
0.451 |
| Experimental |
|
|
Parda |
|
fss.media.math |
12 |
3.259 |
3.278 |
2.111 |
4.333 |
0.589 |
0.170 |
0.374 |
0.722 |
YES |
-0.158 |
-0.653 |
| Experimental |
|
|
Indígena |
|
fss.media.math |
5 |
3.778 |
4.111 |
2.556 |
4.556 |
0.793 |
0.355 |
0.985 |
0.778 |
NO |
-0.507 |
-1.657 |
| Experimental |
|
|
Branca |
|
fss.media.math |
6 |
3.319 |
3.333 |
2.667 |
4.250 |
0.577 |
0.236 |
0.606 |
0.639 |
YES |
0.332 |
-1.468 |
| Experimental |
|
|
Amarela |
|
fss.media.math |
1 |
3.375 |
3.375 |
3.375 |
3.375 |
|
|
|
0.000 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
fss.media.math |
22 |
3.172 |
3.111 |
2.000 |
4.556 |
0.721 |
0.154 |
0.320 |
0.722 |
YES |
0.404 |
-0.949 |
| Controle |
|
|
|
6 ano |
dfs.media.math |
32 |
3.232 |
3.167 |
2.333 |
4.444 |
0.510 |
0.090 |
0.184 |
0.583 |
NO |
0.542 |
-0.503 |
| Controle |
|
|
|
7 ano |
dfs.media.math |
31 |
3.336 |
3.333 |
2.444 |
4.222 |
0.482 |
0.087 |
0.177 |
0.646 |
YES |
-0.050 |
-0.883 |
| Controle |
|
|
|
8 ano |
dfs.media.math |
17 |
3.248 |
3.222 |
2.333 |
4.444 |
0.510 |
0.124 |
0.262 |
0.444 |
YES |
0.431 |
-0.031 |
| Controle |
|
|
|
9 ano |
dfs.media.math |
19 |
3.351 |
3.333 |
2.444 |
4.444 |
0.459 |
0.105 |
0.221 |
0.444 |
YES |
0.392 |
0.252 |
| Experimental |
|
|
|
6 ano |
dfs.media.math |
15 |
3.526 |
3.556 |
2.556 |
4.556 |
0.581 |
0.150 |
0.322 |
0.778 |
YES |
0.091 |
-1.193 |
| Experimental |
|
|
|
7 ano |
dfs.media.math |
11 |
3.303 |
3.333 |
2.556 |
4.222 |
0.533 |
0.161 |
0.358 |
0.833 |
YES |
0.059 |
-1.340 |
| Experimental |
|
|
|
8 ano |
dfs.media.math |
11 |
3.000 |
3.111 |
1.889 |
4.111 |
0.648 |
0.195 |
0.435 |
0.722 |
YES |
-0.138 |
-1.007 |
| Experimental |
|
|
|
9 ano |
dfs.media.math |
9 |
3.136 |
3.222 |
2.222 |
3.778 |
0.582 |
0.194 |
0.447 |
1.000 |
YES |
-0.316 |
-1.709 |
| Controle |
|
|
|
6 ano |
fss.media.math |
32 |
3.452 |
3.444 |
2.444 |
5.000 |
0.585 |
0.103 |
0.211 |
0.604 |
NO |
0.621 |
-0.007 |
| Controle |
|
|
|
7 ano |
fss.media.math |
31 |
3.480 |
3.444 |
2.444 |
5.000 |
0.582 |
0.104 |
0.213 |
0.778 |
YES |
0.463 |
0.060 |
| Controle |
|
|
|
8 ano |
fss.media.math |
17 |
3.291 |
3.111 |
2.444 |
4.556 |
0.551 |
0.134 |
0.283 |
0.667 |
NO |
0.597 |
-0.517 |
| Controle |
|
|
|
9 ano |
fss.media.math |
19 |
3.414 |
3.333 |
2.778 |
4.333 |
0.421 |
0.097 |
0.203 |
0.556 |
YES |
0.310 |
-0.807 |
| Experimental |
|
|
|
6 ano |
fss.media.math |
15 |
3.410 |
3.333 |
2.556 |
4.556 |
0.584 |
0.151 |
0.323 |
0.410 |
NO |
0.663 |
-0.723 |
| Experimental |
|
|
|
7 ano |
fss.media.math |
11 |
3.538 |
3.556 |
2.000 |
4.333 |
0.736 |
0.222 |
0.494 |
0.833 |
NO |
-0.733 |
-0.671 |
| Experimental |
|
|
|
8 ano |
fss.media.math |
11 |
2.960 |
2.778 |
2.222 |
4.556 |
0.680 |
0.205 |
0.457 |
0.722 |
NO |
1.074 |
0.066 |
| Experimental |
|
|
|
9 ano |
fss.media.math |
9 |
3.160 |
3.222 |
2.111 |
4.111 |
0.648 |
0.216 |
0.498 |
0.667 |
YES |
0.037 |
-1.341 |
ANCOVA and Pairwise
for one factor: grupo
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]),], "fss.media.math", "grupo")
pdat.long <- rbind(pdat[,c("id","grupo")], pdat[,c("id","grupo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["flow.math"]] <- c(pdat[["dfs.media.math"]], pdat[["fss.media.math"]])
aov = anova_test(pdat, fss.media.math ~ dfs.media.math + grupo)
laov[["grupo"]] <- get_anova_table(aov)
pwc <- emmeans_test(pdat, fss.media.math ~ grupo, covariate = dfs.media.math,
p.adjust.method = "bonferroni")
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, "grupo"),
flow.math ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- plyr::rbind.fill(pwc, pwc.long)
ds <- get.descriptives(pdat, "fss.media.math", "grupo", covar = "dfs.media.math")
ds <- merge(ds[ds$variable != "dfs.media.math",],
ds[ds$variable == "dfs.media.math", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".dfs.media.math"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.dfs.media.math","se.dfs.media.math","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- ds
Computing
ANCOVA and PairWise After removing non-normal data (OK)
wdat = pdat
res = residuals(lm(fss.media.math ~ dfs.media.math + grupo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo")], wdat[,c("id","grupo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["flow.math"]] <- c(wdat[["dfs.media.math"]], wdat[["fss.media.math"]])
ldat[["grupo"]] = wdat
(non.normal)
## [1] "P1768" "P3630" "P130" "P3013" "P3014" "P2244" "P3508" "P1010" "P510" "P3624" "P1089" "P3598" "P1728" "P3160" "P3495"
## [16] "P2247" "P3578" "P3094" "P622" "P2294" "P3502" "P3031" "P231" "P1016" "P3055" "P2408" "P3116" "P2380" "P1705" "P3027"
## [31] "P3244" "P3516" "P829" "P3577" "P3511" "P1701" "P3002" "P458" "P3566" "P2250" "P3153" "P2240" "P3239" "P612" "P3519"
## [46] "P1698" "P3721" "P2383" "P812" "P523" "P960" "P2390" "P997" "P3091" "P124" "P613" "P2378" "P820" "P3560" "P133"
## [61] "P448" "P184" "P952" "P3615" "P123" "P238" "P232" "P3492" "P1091" "P2376" "P140" "P121" "P2222" "P3098" "P3022"
## [76] "P1764" "P626" "P1742" "P1101" "P457" "P192" "P1094" "P463" "P3093" "P1711" "P3178" "P950" "P3008" "P947" "P3512"
## [91] "P138" "P813" "P3569" "P943" "P3016" "P515" "P3505" "P936" "P3174"
aov = anova_test(wdat, fss.media.math ~ dfs.media.math + grupo)
laov[["grupo"]] <- merge(get_anova_table(aov), laov[["grupo"]],
by="Effect", suffixes = c("","'"))
(df = get_anova_table(aov))
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 dfs.media.math 1 43 70.878 1.22e-10 * 0.622
## 2 grupo 1 43 4.567 3.80e-02 * 0.096
| dfs.media.math |
1 |
43 |
70.878 |
0.000 |
* |
0.622 |
| grupo |
1 |
43 |
4.567 |
0.038 |
* |
0.096 |
pwc <- emmeans_test(wdat, fss.media.math ~ grupo, covariate = dfs.media.math,
p.adjust.method = "bonferroni")
| dfs.media.math*grupo |
fss.media.math |
Controle |
Experimental |
43 |
2.137 |
0.038 |
0.038 |
* |
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, "grupo"),
flow.math ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- merge(plyr::rbind.fill(pwc, pwc.long), lpwc[["grupo"]],
by=c("grupo","term",".y.","group1","group2"),
suffixes = c("","'"))
| Controle |
time |
flow.math |
pre |
pos |
88 |
-5.554 |
0.000 |
0.000 |
**** |
| Experimental |
time |
flow.math |
pre |
pos |
88 |
-2.017 |
0.047 |
0.047 |
* |
ds <- get.descriptives(wdat, "fss.media.math", "grupo", covar = "dfs.media.math")
ds <- merge(ds[ds$variable != "dfs.media.math",],
ds[ds$variable == "dfs.media.math", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".dfs.media.math"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.dfs.media.math","se.dfs.media.math","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- merge(ds, lemms[["grupo"]], by=c("grupo"), suffixes = c("","'"))
| Controle |
30 |
3.259 |
0.095 |
3.916 |
0.061 |
3.986 |
0.042 |
3.900 |
4.071 |
| Experimental |
16 |
3.632 |
0.140 |
3.958 |
0.105 |
3.827 |
0.059 |
3.708 |
3.946 |
Plots for ancova
plots <- oneWayAncovaPlots(
wdat, "fss.media.math", "grupo", aov, list("grupo"=pwc), addParam = c("mean_ci"),
font.label.size=10, step.increase=0.05, p.label="p.adj",
subtitle = which(aov$Effect == "grupo"))
if (!is.null(nrow(plots[["grupo"]]$data)))
plots[["grupo"]] +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)

plots <- oneWayAncovaBoxPlots(
wdat, "fss.media.math", "grupo", aov, pwc, covar = "dfs.media.math",
theme = "classic", color = color[["grupo"]],
subtitle = which(aov$Effect == "grupo"))
if (length(unique(wdat[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("flow (math)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

if (length(unique(wdat.long[["grupo"]])) > 1)
plots <- oneWayAncovaBoxPlots(
wdat.long, "flow.math", "grupo", aov, pwc.long,
pre.post = "time", theme = "classic", color = color$prepost)
if (length(unique(wdat.long[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("flow (math)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
ggscatter(wdat, x = "dfs.media.math", y = "fss.media.math", size = 0.5,
color = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking normality and
homogeneity
res <- augment(lm(fss.media.math ~ dfs.media.math + grupo, data = wdat))
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.948 0.0409
levene_test(res, .resid ~ grupo)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 1 44 0.0726 0.789
ANCOVA and
Pairwise for two factors grupo:Sexo
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Sexo"]]),],
"fss.media.math", c("grupo","Sexo"))
pdat = pdat[pdat[["Sexo"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Sexo"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Sexo"]] = factor(
pdat[["Sexo"]],
level[["Sexo"]][level[["Sexo"]] %in% unique(pdat[["Sexo"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Sexo")], pdat[,c("id","grupo","Sexo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["flow.math"]] <- c(pdat[["dfs.media.math"]], pdat[["fss.media.math"]])
if (length(unique(pdat[["Sexo"]])) >= 2) {
aov = anova_test(pdat, fss.media.math ~ dfs.media.math + grupo*Sexo)
laov[["grupo:Sexo"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwcs <- list()
pwcs[["Sexo"]] <- emmeans_test(
group_by(pdat, grupo), fss.media.math ~ Sexo,
covariate = dfs.media.math, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Sexo), fss.media.math ~ grupo,
covariate = dfs.media.math, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Sexo"]])
pwc <- pwc[,c("grupo","Sexo", colnames(pwc)[!colnames(pwc) %in% c("grupo","Sexo")])]
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Sexo")),
flow.math ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Sexo"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
ds <- get.descriptives(pdat, "fss.media.math", c("grupo","Sexo"), covar = "dfs.media.math")
ds <- merge(ds[ds$variable != "dfs.media.math",],
ds[ds$variable == "dfs.media.math", !colnames(ds) %in% c("variable")],
by = c("grupo","Sexo"), all.x = T, suffixes = c("", ".dfs.media.math"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Sexo"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Sexo","n","mean.dfs.media.math","se.dfs.media.math","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Sexo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Sexo"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Sexo"]])) >= 2) {
wdat = pdat
res = residuals(lm(fss.media.math ~ dfs.media.math + grupo*Sexo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Sexo")], wdat[,c("id","grupo","Sexo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["flow.math"]] <- c(wdat[["dfs.media.math"]], wdat[["fss.media.math"]])
ldat[["grupo:Sexo"]] = wdat
(non.normal)
}
## [1] "P3013" "P1768" "P3630" "P2244" "P130" "P3508" "P1010" "P3014" "P3624" "P1089" "P510" "P3160" "P3495" "P3598" "P1728"
## [16] "P2247" "P3502" "P231" "P3578" "P622" "P3094" "P2294" "P2380" "P1705" "P3027" "P3031" "P1016" "P3055" "P2408" "P3244"
## [31] "P3516" "P3577" "P3116" "P3566" "P2250" "P2240" "P3239" "P829" "P612" "P1698" "P3519" "P3511" "P3002" "P458" "P3091"
## [46] "P3153" "P1701" "P3721" "P812" "P960" "P2390" "P997" "P2383" "P613" "P3093" "P820" "P523" "P124" "P3178" "P1711"
## [61] "P133" "P2378" "P448" "P238" "P463" "P232" "P1091" "P184" "P3560" "P3615" "P123" "P952" "P121" "P3492" "P140"
## [76] "P3098" "P2376" "P192" "P2222" "P1094" "P626" "P3022" "P1742" "P1764" "P3008" "P947" "P1101" "P138" "P1762" "P521"
if (length(unique(pdat[["Sexo"]])) >= 2) {
aov = anova_test(wdat, fss.media.math ~ dfs.media.math + grupo*Sexo)
laov[["grupo:Sexo"]] <- merge(get_anova_table(aov), laov[["grupo:Sexo"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| dfs.media.math |
1 |
50 |
98.948 |
0.000 |
* |
0.664 |
| grupo |
1 |
50 |
6.031 |
0.018 |
* |
0.108 |
| Sexo |
1 |
50 |
0.052 |
0.820 |
|
0.001 |
| grupo:Sexo |
1 |
50 |
1.515 |
0.224 |
|
0.029 |
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwcs <- list()
pwcs[["Sexo"]] <- emmeans_test(
group_by(wdat, grupo), fss.media.math ~ Sexo,
covariate = dfs.media.math, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Sexo), fss.media.math ~ grupo,
covariate = dfs.media.math, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Sexo"]])
pwc <- pwc[,c("grupo","Sexo", colnames(pwc)[!colnames(pwc) %in% c("grupo","Sexo")])]
}
|
F |
dfs.media.math*grupo |
fss.media.math |
Controle |
Experimental |
50 |
0.687 |
0.495 |
0.495 |
ns |
|
M |
dfs.media.math*grupo |
fss.media.math |
Controle |
Experimental |
50 |
2.674 |
0.010 |
0.010 |
* |
| Controle |
|
dfs.media.math*Sexo |
fss.media.math |
F |
M |
50 |
-0.900 |
0.373 |
0.373 |
ns |
| Experimental |
|
dfs.media.math*Sexo |
fss.media.math |
F |
M |
50 |
0.886 |
0.380 |
0.380 |
ns |
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Sexo")),
flow.math ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Sexo"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Sexo"]],
by=c("grupo","Sexo","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
F |
time |
flow.math |
pre |
pos |
102 |
-2.851 |
0.005 |
0.005 |
** |
| Controle |
M |
time |
flow.math |
pre |
pos |
102 |
-4.463 |
0.000 |
0.000 |
**** |
| Experimental |
F |
time |
flow.math |
pre |
pos |
102 |
-1.488 |
0.140 |
0.140 |
ns |
| Experimental |
M |
time |
flow.math |
pre |
pos |
102 |
-0.929 |
0.355 |
0.355 |
ns |
if (length(unique(pdat[["Sexo"]])) >= 2) {
ds <- get.descriptives(wdat, "fss.media.math", c("grupo","Sexo"), covar = "dfs.media.math")
ds <- merge(ds[ds$variable != "dfs.media.math",],
ds[ds$variable == "dfs.media.math", !colnames(ds) %in% c("variable")],
by = c("grupo","Sexo"), all.x = T, suffixes = c("", ".dfs.media.math"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Sexo"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Sexo","n","mean.dfs.media.math","se.dfs.media.math",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Sexo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Sexo"]] <- merge(ds, lemms[["grupo:Sexo"]],
by=c("grupo","Sexo"), suffixes = c("","'"))
}
| Controle |
F |
15 |
3.356 |
0.117 |
3.833 |
0.081 |
3.837 |
0.058 |
3.720 |
3.954 |
| Controle |
M |
22 |
3.177 |
0.116 |
3.794 |
0.079 |
3.906 |
0.049 |
3.806 |
4.005 |
| Experimental |
F |
7 |
3.460 |
0.263 |
3.825 |
0.189 |
3.766 |
0.086 |
3.594 |
3.938 |
| Experimental |
M |
11 |
3.677 |
0.129 |
3.859 |
0.131 |
3.668 |
0.071 |
3.526 |
3.811 |
Plots for ancova
if (length(unique(pdat[["Sexo"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Sexo", aov, ylab = "flow (math)",
subtitle = which(aov$Effect == "grupo:Sexo"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Sexo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Sexo"]])) >= 2) {
ggPlotAoC2(pwcs, "Sexo", "grupo", aov, ylab = "flow (math)",
subtitle = which(aov$Effect == "grupo:Sexo"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Sexo"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "fss.media.math", c("grupo","Sexo"), aov, pwcs, covar = "dfs.media.math",
theme = "classic", color = color[["grupo:Sexo"]],
subtitle = which(aov$Effect == "grupo:Sexo"))
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
plots[["grupo:Sexo"]] + ggplot2::ylab("flow (math)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["Sexo"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "flow.math", c("grupo","Sexo"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Sexo"]])) >= 2)
plots[["grupo:Sexo"]] + ggplot2::ylab("flow (math)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Sexo"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.math", y = "fss.media.math", size = 0.5,
facet.by = c("grupo","Sexo"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Sexo"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.math", y = "fss.media.math", size = 0.5,
color = "grupo", facet.by = "Sexo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Sexo"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Sexo"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.math", y = "fss.media.math", size = 0.5,
color = "Sexo", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Sexo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Sexo"))) +
ggplot2::scale_color_manual(values = color[["Sexo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Sexo"]])) >= 2)
res <- augment(lm(fss.media.math ~ dfs.media.math + grupo*Sexo, data = wdat))
if (length(unique(pdat[["Sexo"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.956 0.0421
if (length(unique(pdat[["Sexo"]])) >= 2)
levene_test(res, .resid ~ grupo*Sexo)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 51 0.800 0.500
ANCOVA and
Pairwise for two factors grupo:Zona
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Zona"]]),],
"fss.media.math", c("grupo","Zona"))
pdat = pdat[pdat[["Zona"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Zona"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Zona"]] = factor(
pdat[["Zona"]],
level[["Zona"]][level[["Zona"]] %in% unique(pdat[["Zona"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Zona")], pdat[,c("id","grupo","Zona")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["flow.math"]] <- c(pdat[["dfs.media.math"]], pdat[["fss.media.math"]])
if (length(unique(pdat[["Zona"]])) >= 2) {
aov = anova_test(pdat, fss.media.math ~ dfs.media.math + grupo*Zona)
laov[["grupo:Zona"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Zona"]])) >= 2) {
pwcs <- list()
pwcs[["Zona"]] <- emmeans_test(
group_by(pdat, grupo), fss.media.math ~ Zona,
covariate = dfs.media.math, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Zona), fss.media.math ~ grupo,
covariate = dfs.media.math, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Zona"]])
pwc <- pwc[,c("grupo","Zona", colnames(pwc)[!colnames(pwc) %in% c("grupo","Zona")])]
}
if (length(unique(pdat[["Zona"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Zona")),
flow.math ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Zona"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Zona"]])) >= 2) {
ds <- get.descriptives(pdat, "fss.media.math", c("grupo","Zona"), covar = "dfs.media.math")
ds <- merge(ds[ds$variable != "dfs.media.math",],
ds[ds$variable == "dfs.media.math", !colnames(ds) %in% c("variable")],
by = c("grupo","Zona"), all.x = T, suffixes = c("", ".dfs.media.math"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Zona"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Zona","n","mean.dfs.media.math","se.dfs.media.math","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Zona", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Zona"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Zona"]])) >= 2) {
wdat = pdat
res = residuals(lm(fss.media.math ~ dfs.media.math + grupo*Zona, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Zona")], wdat[,c("id","grupo","Zona")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["flow.math"]] <- c(wdat[["dfs.media.math"]], wdat[["fss.media.math"]])
ldat[["grupo:Zona"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["Zona"]])) >= 2) {
aov = anova_test(wdat, fss.media.math ~ dfs.media.math + grupo*Zona)
laov[["grupo:Zona"]] <- merge(get_anova_table(aov), laov[["grupo:Zona"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| dfs.media.math |
1 |
103 |
29.791 |
0.000 |
* |
0.224 |
| grupo |
1 |
103 |
2.673 |
0.105 |
|
0.025 |
| Zona |
1 |
103 |
3.051 |
0.084 |
|
0.029 |
| grupo:Zona |
1 |
103 |
0.024 |
0.878 |
|
0.000 |
if (length(unique(pdat[["Zona"]])) >= 2) {
pwcs <- list()
pwcs[["Zona"]] <- emmeans_test(
group_by(wdat, grupo), fss.media.math ~ Zona,
covariate = dfs.media.math, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Zona), fss.media.math ~ grupo,
covariate = dfs.media.math, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Zona"]])
pwc <- pwc[,c("grupo","Zona", colnames(pwc)[!colnames(pwc) %in% c("grupo","Zona")])]
}
|
Rural |
dfs.media.math*grupo |
fss.media.math |
Controle |
Experimental |
103 |
1.364 |
0.175 |
0.175 |
ns |
|
Urbana |
dfs.media.math*grupo |
fss.media.math |
Controle |
Experimental |
103 |
0.899 |
0.371 |
0.371 |
ns |
| Controle |
|
dfs.media.math*Zona |
fss.media.math |
Rural |
Urbana |
103 |
-1.537 |
0.127 |
0.127 |
ns |
| Experimental |
|
dfs.media.math*Zona |
fss.media.math |
Rural |
Urbana |
103 |
-0.856 |
0.394 |
0.394 |
ns |
if (length(unique(pdat[["Zona"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Zona")),
flow.math ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Zona"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Zona"]],
by=c("grupo","Zona","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Rural |
time |
flow.math |
pre |
pos |
208 |
-1.169 |
0.244 |
0.244 |
ns |
| Controle |
Urbana |
time |
flow.math |
pre |
pos |
208 |
-1.943 |
0.053 |
0.053 |
ns |
| Experimental |
Rural |
time |
flow.math |
pre |
pos |
208 |
0.525 |
0.600 |
0.600 |
ns |
| Experimental |
Urbana |
time |
flow.math |
pre |
pos |
208 |
-1.250 |
0.213 |
0.213 |
ns |
if (length(unique(pdat[["Zona"]])) >= 2) {
ds <- get.descriptives(wdat, "fss.media.math", c("grupo","Zona"), covar = "dfs.media.math")
ds <- merge(ds[ds$variable != "dfs.media.math",],
ds[ds$variable == "dfs.media.math", !colnames(ds) %in% c("variable")],
by = c("grupo","Zona"), all.x = T, suffixes = c("", ".dfs.media.math"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Zona"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Zona","n","mean.dfs.media.math","se.dfs.media.math",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Zona", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Zona"]] <- merge(ds, lemms[["grupo:Zona"]],
by=c("grupo","Zona"), suffixes = c("","'"))
}
| Controle |
Rural |
54 |
3.274 |
0.075 |
3.406 |
0.072 |
3.396 |
0.074 |
3.249 |
3.543 |
| Controle |
Urbana |
17 |
3.222 |
0.116 |
3.611 |
0.182 |
3.629 |
0.132 |
3.367 |
3.891 |
| Experimental |
Rural |
29 |
3.360 |
0.097 |
3.280 |
0.121 |
3.224 |
0.102 |
3.022 |
3.426 |
| Experimental |
Urbana |
8 |
2.819 |
0.259 |
3.184 |
0.260 |
3.416 |
0.197 |
3.024 |
3.807 |
Plots for ancova
if (length(unique(pdat[["Zona"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Zona", aov, ylab = "flow (math)",
subtitle = which(aov$Effect == "grupo:Zona"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Zona"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Zona"]])) >= 2) {
ggPlotAoC2(pwcs, "Zona", "grupo", aov, ylab = "flow (math)",
subtitle = which(aov$Effect == "grupo:Zona"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Zona"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "fss.media.math", c("grupo","Zona"), aov, pwcs, covar = "dfs.media.math",
theme = "classic", color = color[["grupo:Zona"]],
subtitle = which(aov$Effect == "grupo:Zona"))
}
if (length(unique(pdat[["Zona"]])) >= 2) {
plots[["grupo:Zona"]] + ggplot2::ylab("flow (math)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["Zona"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "flow.math", c("grupo","Zona"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Zona"]])) >= 2)
plots[["grupo:Zona"]] + ggplot2::ylab("flow (math)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Zona"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.math", y = "fss.media.math", size = 0.5,
facet.by = c("grupo","Zona"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Zona"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.math", y = "fss.media.math", size = 0.5,
color = "grupo", facet.by = "Zona", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Zona"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Zona"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.math", y = "fss.media.math", size = 0.5,
color = "Zona", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Zona)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Zona"))) +
ggplot2::scale_color_manual(values = color[["Zona"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Zona"]])) >= 2)
res <- augment(lm(fss.media.math ~ dfs.media.math + grupo*Zona, data = wdat))
if (length(unique(pdat[["Zona"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.980 0.0971
if (length(unique(pdat[["Zona"]])) >= 2)
levene_test(res, .resid ~ grupo*Zona)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 104 1.83 0.146
ANCOVA and
Pairwise for two factors grupo:Cor.Raca
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Cor.Raca"]]),],
"fss.media.math", c("grupo","Cor.Raca"))
## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
pdat = pdat[pdat[["Cor.Raca"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Cor.Raca"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Cor.Raca"]] = factor(
pdat[["Cor.Raca"]],
level[["Cor.Raca"]][level[["Cor.Raca"]] %in% unique(pdat[["Cor.Raca"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Cor.Raca")], pdat[,c("id","grupo","Cor.Raca")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["flow.math"]] <- c(pdat[["dfs.media.math"]], pdat[["fss.media.math"]])
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
aov = anova_test(pdat, fss.media.math ~ dfs.media.math + grupo*Cor.Raca)
laov[["grupo:Cor.Raca"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwcs <- list()
pwcs[["Cor.Raca"]] <- emmeans_test(
group_by(pdat, grupo), fss.media.math ~ Cor.Raca,
covariate = dfs.media.math, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Cor.Raca), fss.media.math ~ grupo,
covariate = dfs.media.math, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Cor.Raca"]])
pwc <- pwc[,c("grupo","Cor.Raca", colnames(pwc)[!colnames(pwc) %in% c("grupo","Cor.Raca")])]
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Cor.Raca")),
flow.math ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Cor.Raca"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ds <- get.descriptives(pdat, "fss.media.math", c("grupo","Cor.Raca"), covar = "dfs.media.math")
ds <- merge(ds[ds$variable != "dfs.media.math",],
ds[ds$variable == "dfs.media.math", !colnames(ds) %in% c("variable")],
by = c("grupo","Cor.Raca"), all.x = T, suffixes = c("", ".dfs.media.math"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Cor.Raca"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Cor.Raca","n","mean.dfs.media.math","se.dfs.media.math","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Cor.Raca", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Cor.Raca"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
wdat = pdat
res = residuals(lm(fss.media.math ~ dfs.media.math + grupo*Cor.Raca, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Cor.Raca")], wdat[,c("id","grupo","Cor.Raca")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["flow.math"]] <- c(wdat[["dfs.media.math"]], wdat[["fss.media.math"]])
ldat[["grupo:Cor.Raca"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
aov = anova_test(wdat, fss.media.math ~ dfs.media.math + grupo*Cor.Raca)
laov[["grupo:Cor.Raca"]] <- merge(get_anova_table(aov), laov[["grupo:Cor.Raca"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| dfs.media.math |
1 |
68 |
12.974 |
0.001 |
* |
0.160 |
| grupo |
1 |
68 |
1.221 |
0.273 |
|
0.018 |
| Cor.Raca |
1 |
68 |
0.073 |
0.787 |
|
0.001 |
| grupo:Cor.Raca |
1 |
68 |
0.266 |
0.607 |
|
0.004 |
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwcs <- list()
pwcs[["Cor.Raca"]] <- emmeans_test(
group_by(wdat, grupo), fss.media.math ~ Cor.Raca,
covariate = dfs.media.math, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Cor.Raca), fss.media.math ~ grupo,
covariate = dfs.media.math, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Cor.Raca"]])
pwc <- pwc[,c("grupo","Cor.Raca", colnames(pwc)[!colnames(pwc) %in% c("grupo","Cor.Raca")])]
}
|
Parda |
dfs.media.math*grupo |
fss.media.math |
Controle |
Experimental |
68 |
1.212 |
0.230 |
0.230 |
ns |
|
Branca |
dfs.media.math*grupo |
fss.media.math |
Controle |
Experimental |
68 |
0.136 |
0.892 |
0.892 |
ns |
| Controle |
|
dfs.media.math*Cor.Raca |
fss.media.math |
Parda |
Branca |
68 |
0.085 |
0.932 |
0.932 |
ns |
| Experimental |
|
dfs.media.math*Cor.Raca |
fss.media.math |
Parda |
Branca |
68 |
-0.577 |
0.566 |
0.566 |
ns |
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Cor.Raca")),
flow.math ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Cor.Raca"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Cor.Raca"]],
by=c("grupo","Cor.Raca","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Parda |
time |
flow.math |
pre |
pos |
138 |
-1.127 |
0.262 |
0.262 |
ns |
| Controle |
Branca |
time |
flow.math |
pre |
pos |
138 |
-0.328 |
0.743 |
0.743 |
ns |
| Experimental |
Parda |
time |
flow.math |
pre |
pos |
138 |
0.243 |
0.808 |
0.808 |
ns |
| Experimental |
Branca |
time |
flow.math |
pre |
pos |
138 |
-0.703 |
0.483 |
0.483 |
ns |
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ds <- get.descriptives(wdat, "fss.media.math", c("grupo","Cor.Raca"), covar = "dfs.media.math")
ds <- merge(ds[ds$variable != "dfs.media.math",],
ds[ds$variable == "dfs.media.math", !colnames(ds) %in% c("variable")],
by = c("grupo","Cor.Raca"), all.x = T, suffixes = c("", ".dfs.media.math"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Cor.Raca"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Cor.Raca","n","mean.dfs.media.math","se.dfs.media.math",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Cor.Raca", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Cor.Raca"]] <- merge(ds, lemms[["grupo:Cor.Raca"]],
by=c("grupo","Cor.Raca"), suffixes = c("","'"))
}
| Controle |
Branca |
9 |
3.395 |
0.184 |
3.481 |
0.201 |
3.453 |
0.174 |
3.105 |
3.801 |
| Controle |
Parda |
46 |
3.346 |
0.081 |
3.478 |
0.081 |
3.469 |
0.077 |
3.316 |
3.623 |
| Experimental |
Branca |
6 |
3.093 |
0.232 |
3.319 |
0.236 |
3.415 |
0.215 |
2.986 |
3.845 |
| Experimental |
Parda |
12 |
3.315 |
0.156 |
3.259 |
0.170 |
3.264 |
0.151 |
2.963 |
3.565 |
Plots for ancova
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Cor.Raca", aov, ylab = "flow (math)",
subtitle = which(aov$Effect == "grupo:Cor.Raca"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Cor.Raca"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggPlotAoC2(pwcs, "Cor.Raca", "grupo", aov, ylab = "flow (math)",
subtitle = which(aov$Effect == "grupo:Cor.Raca"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "fss.media.math", c("grupo","Cor.Raca"), aov, pwcs, covar = "dfs.media.math",
theme = "classic", color = color[["grupo:Cor.Raca"]],
subtitle = which(aov$Effect == "grupo:Cor.Raca"))
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
plots[["grupo:Cor.Raca"]] + ggplot2::ylab("flow (math)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "flow.math", c("grupo","Cor.Raca"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
plots[["grupo:Cor.Raca"]] + ggplot2::ylab("flow (math)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.math", y = "fss.media.math", size = 0.5,
facet.by = c("grupo","Cor.Raca"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.math", y = "fss.media.math", size = 0.5,
color = "grupo", facet.by = "Cor.Raca", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Cor.Raca"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.math", y = "fss.media.math", size = 0.5,
color = "Cor.Raca", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Cor.Raca)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Cor.Raca"))) +
ggplot2::scale_color_manual(values = color[["Cor.Raca"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
res <- augment(lm(fss.media.math ~ dfs.media.math + grupo*Cor.Raca, data = wdat))
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.979 0.256
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
levene_test(res, .resid ~ grupo*Cor.Raca)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 69 0.141 0.935
ANCOVA and
Pairwise for two factors grupo:Serie
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Serie"]]),],
"fss.media.math", c("grupo","Serie"))
pdat = pdat[pdat[["Serie"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Serie"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Serie"]] = factor(
pdat[["Serie"]],
level[["Serie"]][level[["Serie"]] %in% unique(pdat[["Serie"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Serie")], pdat[,c("id","grupo","Serie")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["flow.math"]] <- c(pdat[["dfs.media.math"]], pdat[["fss.media.math"]])
if (length(unique(pdat[["Serie"]])) >= 2) {
aov = anova_test(pdat, fss.media.math ~ dfs.media.math + grupo*Serie)
laov[["grupo:Serie"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Serie"]])) >= 2) {
pwcs <- list()
pwcs[["Serie"]] <- emmeans_test(
group_by(pdat, grupo), fss.media.math ~ Serie,
covariate = dfs.media.math, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Serie), fss.media.math ~ grupo,
covariate = dfs.media.math, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Serie"]])
pwc <- pwc[,c("grupo","Serie", colnames(pwc)[!colnames(pwc) %in% c("grupo","Serie")])]
}
if (length(unique(pdat[["Serie"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Serie")),
flow.math ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Serie"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Serie"]])) >= 2) {
ds <- get.descriptives(pdat, "fss.media.math", c("grupo","Serie"), covar = "dfs.media.math")
ds <- merge(ds[ds$variable != "dfs.media.math",],
ds[ds$variable == "dfs.media.math", !colnames(ds) %in% c("variable")],
by = c("grupo","Serie"), all.x = T, suffixes = c("", ".dfs.media.math"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Serie"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Serie","n","mean.dfs.media.math","se.dfs.media.math","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Serie", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Serie"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Serie"]])) >= 2) {
wdat = pdat
res = residuals(lm(fss.media.math ~ dfs.media.math + grupo*Serie, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Serie")], wdat[,c("id","grupo","Serie")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["flow.math"]] <- c(wdat[["dfs.media.math"]], wdat[["fss.media.math"]])
ldat[["grupo:Serie"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["Serie"]])) >= 2) {
aov = anova_test(wdat, fss.media.math ~ dfs.media.math + grupo*Serie)
laov[["grupo:Serie"]] <- merge(get_anova_table(aov), laov[["grupo:Serie"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| dfs.media.math |
1 |
136 |
32.072 |
0.000 |
* |
0.191 |
| grupo |
1 |
136 |
1.490 |
0.224 |
|
0.011 |
| Serie |
3 |
136 |
1.218 |
0.305 |
|
0.026 |
| grupo:Serie |
3 |
136 |
0.486 |
0.693 |
|
0.011 |
if (length(unique(pdat[["Serie"]])) >= 2) {
pwcs <- list()
pwcs[["Serie"]] <- emmeans_test(
group_by(wdat, grupo), fss.media.math ~ Serie,
covariate = dfs.media.math, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Serie), fss.media.math ~ grupo,
covariate = dfs.media.math, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Serie"]])
pwc <- pwc[,c("grupo","Serie", colnames(pwc)[!colnames(pwc) %in% c("grupo","Serie")])]
}
|
6 ano |
dfs.media.math*grupo |
fss.media.math |
Controle |
Experimental |
136 |
1.110 |
0.269 |
0.269 |
ns |
|
7 ano |
dfs.media.math*grupo |
fss.media.math |
Controle |
Experimental |
136 |
-0.398 |
0.691 |
0.691 |
ns |
|
8 ano |
dfs.media.math*grupo |
fss.media.math |
Controle |
Experimental |
136 |
1.019 |
0.310 |
0.310 |
ns |
|
9 ano |
dfs.media.math*grupo |
fss.media.math |
Controle |
Experimental |
136 |
0.688 |
0.493 |
0.493 |
ns |
| Controle |
|
dfs.media.math*Serie |
fss.media.math |
6 ano |
7 ano |
136 |
0.172 |
0.864 |
1.000 |
ns |
| Controle |
|
dfs.media.math*Serie |
fss.media.math |
6 ano |
8 ano |
136 |
1.067 |
0.288 |
1.000 |
ns |
| Controle |
|
dfs.media.math*Serie |
fss.media.math |
6 ano |
9 ano |
136 |
0.630 |
0.530 |
1.000 |
ns |
| Controle |
|
dfs.media.math*Serie |
fss.media.math |
7 ano |
8 ano |
136 |
0.916 |
0.361 |
1.000 |
ns |
| Controle |
|
dfs.media.math*Serie |
fss.media.math |
7 ano |
9 ano |
136 |
0.479 |
0.633 |
1.000 |
ns |
| Controle |
|
dfs.media.math*Serie |
fss.media.math |
8 ano |
9 ano |
136 |
-0.410 |
0.682 |
1.000 |
ns |
| Experimental |
|
dfs.media.math*Serie |
fss.media.math |
6 ano |
7 ano |
136 |
-1.123 |
0.263 |
1.000 |
ns |
| Experimental |
|
dfs.media.math*Serie |
fss.media.math |
6 ano |
8 ano |
136 |
0.900 |
0.370 |
1.000 |
ns |
| Experimental |
|
dfs.media.math*Serie |
fss.media.math |
6 ano |
9 ano |
136 |
0.261 |
0.795 |
1.000 |
ns |
| Experimental |
|
dfs.media.math*Serie |
fss.media.math |
7 ano |
8 ano |
136 |
1.894 |
0.060 |
0.362 |
ns |
| Experimental |
|
dfs.media.math*Serie |
fss.media.math |
7 ano |
9 ano |
136 |
1.241 |
0.217 |
1.000 |
ns |
| Experimental |
|
dfs.media.math*Serie |
fss.media.math |
8 ano |
9 ano |
136 |
-0.565 |
0.573 |
1.000 |
ns |
if (length(unique(pdat[["Serie"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Serie")),
flow.math ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Serie"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Serie"]],
by=c("grupo","Serie","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
6 ano |
time |
flow.math |
pre |
pos |
274 |
-1.587 |
0.114 |
0.114 |
ns |
| Controle |
7 ano |
time |
flow.math |
pre |
pos |
274 |
-1.023 |
0.307 |
0.307 |
ns |
| Controle |
8 ano |
time |
flow.math |
pre |
pos |
274 |
-0.223 |
0.824 |
0.824 |
ns |
| Controle |
9 ano |
time |
flow.math |
pre |
pos |
274 |
-0.348 |
0.728 |
0.728 |
ns |
| Experimental |
6 ano |
time |
flow.math |
pre |
pos |
274 |
0.571 |
0.569 |
0.569 |
ns |
| Experimental |
7 ano |
time |
flow.math |
pre |
pos |
274 |
-0.992 |
0.322 |
0.322 |
ns |
| Experimental |
8 ano |
time |
flow.math |
pre |
pos |
274 |
0.171 |
0.865 |
0.865 |
ns |
| Experimental |
9 ano |
time |
flow.math |
pre |
pos |
274 |
-0.094 |
0.925 |
0.925 |
ns |
if (length(unique(pdat[["Serie"]])) >= 2) {
ds <- get.descriptives(wdat, "fss.media.math", c("grupo","Serie"), covar = "dfs.media.math")
ds <- merge(ds[ds$variable != "dfs.media.math",],
ds[ds$variable == "dfs.media.math", !colnames(ds) %in% c("variable")],
by = c("grupo","Serie"), all.x = T, suffixes = c("", ".dfs.media.math"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Serie"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Serie","n","mean.dfs.media.math","se.dfs.media.math",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Serie", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Serie"]] <- merge(ds, lemms[["grupo:Serie"]],
by=c("grupo","Serie"), suffixes = c("","'"))
}
| Controle |
6 ano |
32 |
3.232 |
0.090 |
3.452 |
0.103 |
3.477 |
0.094 |
3.292 |
3.663 |
| Controle |
7 ano |
31 |
3.336 |
0.087 |
3.480 |
0.104 |
3.455 |
0.095 |
3.266 |
3.643 |
| Controle |
8 ano |
17 |
3.248 |
0.124 |
3.291 |
0.134 |
3.308 |
0.128 |
3.054 |
3.562 |
| Controle |
9 ano |
19 |
3.351 |
0.105 |
3.414 |
0.097 |
3.381 |
0.121 |
3.141 |
3.621 |
| Experimental |
6 ano |
15 |
3.526 |
0.150 |
3.410 |
0.151 |
3.292 |
0.138 |
3.019 |
3.565 |
| Experimental |
7 ano |
11 |
3.303 |
0.161 |
3.538 |
0.222 |
3.528 |
0.159 |
3.213 |
3.844 |
| Experimental |
8 ano |
11 |
3.000 |
0.195 |
2.960 |
0.205 |
3.098 |
0.161 |
2.779 |
3.417 |
| Experimental |
9 ano |
9 |
3.136 |
0.194 |
3.160 |
0.216 |
3.233 |
0.177 |
2.883 |
3.582 |
Plots for ancova
if (length(unique(pdat[["Serie"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Serie", aov, ylab = "flow (math)",
subtitle = which(aov$Effect == "grupo:Serie"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Serie"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Serie"]])) >= 2) {
ggPlotAoC2(pwcs, "Serie", "grupo", aov, ylab = "flow (math)",
subtitle = which(aov$Effect == "grupo:Serie"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Serie"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "fss.media.math", c("grupo","Serie"), aov, pwcs, covar = "dfs.media.math",
theme = "classic", color = color[["grupo:Serie"]],
subtitle = which(aov$Effect == "grupo:Serie"))
}
if (length(unique(pdat[["Serie"]])) >= 2) {
plots[["grupo:Serie"]] + ggplot2::ylab("flow (math)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "flow.math", c("grupo","Serie"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Serie"]])) >= 2)
plots[["grupo:Serie"]] + ggplot2::ylab("flow (math)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Serie"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.math", y = "fss.media.math", size = 0.5,
facet.by = c("grupo","Serie"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.math", y = "fss.media.math", size = 0.5,
color = "grupo", facet.by = "Serie", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Serie"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.math", y = "fss.media.math", size = 0.5,
color = "Serie", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Serie)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Serie"))) +
ggplot2::scale_color_manual(values = color[["Serie"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Serie"]])) >= 2)
res <- augment(lm(fss.media.math ~ dfs.media.math + grupo*Serie, data = wdat))
if (length(unique(pdat[["Serie"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.986 0.151
if (length(unique(pdat[["Serie"]])) >= 2)
levene_test(res, .resid ~ grupo*Serie)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 7 137 0.477 0.850
Summary of Results
Descriptive Statistics
df <- get.descriptives(ldat[["grupo"]], c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1 && paste0("grupo:",f) %in% names(ldat))
get.descriptives(ldat[[paste0("grupo:",f)]], c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
dfs.media.math |
30 |
3.259 |
3.167 |
2.333 |
4.444 |
0.520 |
0.095 |
0.194 |
0.528 |
YES |
0.470 |
0.087 |
| Experimental |
|
|
|
|
dfs.media.math |
16 |
3.632 |
3.722 |
2.667 |
4.556 |
0.560 |
0.140 |
0.299 |
0.833 |
YES |
-0.138 |
-1.198 |
|
|
|
|
|
dfs.media.math |
46 |
3.389 |
3.333 |
2.333 |
4.556 |
0.558 |
0.082 |
0.166 |
0.750 |
YES |
0.269 |
-0.590 |
| Controle |
|
|
|
|
fss.media.math |
30 |
3.916 |
3.889 |
3.444 |
4.667 |
0.335 |
0.061 |
0.125 |
0.333 |
NO |
0.659 |
-0.538 |
| Experimental |
|
|
|
|
fss.media.math |
16 |
3.958 |
4.056 |
3.333 |
4.556 |
0.419 |
0.105 |
0.224 |
0.778 |
YES |
-0.089 |
-1.562 |
|
|
|
|
|
fss.media.math |
46 |
3.931 |
3.889 |
3.333 |
4.667 |
0.363 |
0.053 |
0.108 |
0.556 |
YES |
0.326 |
-0.981 |
| Controle |
F |
|
|
|
dfs.media.math |
15 |
3.356 |
3.222 |
2.778 |
4.444 |
0.455 |
0.117 |
0.252 |
0.389 |
NO |
1.004 |
0.045 |
| Controle |
M |
|
|
|
dfs.media.math |
22 |
3.177 |
3.111 |
2.333 |
4.444 |
0.545 |
0.116 |
0.242 |
0.556 |
YES |
0.365 |
-0.289 |
| Experimental |
F |
|
|
|
dfs.media.math |
7 |
3.460 |
3.333 |
2.667 |
4.556 |
0.695 |
0.263 |
0.643 |
0.944 |
YES |
0.313 |
-1.624 |
| Experimental |
M |
|
|
|
dfs.media.math |
11 |
3.677 |
3.778 |
3.000 |
4.333 |
0.429 |
0.129 |
0.288 |
0.556 |
YES |
0.070 |
-1.433 |
| Controle |
F |
|
|
|
fss.media.math |
15 |
3.833 |
3.778 |
3.444 |
4.333 |
0.312 |
0.081 |
0.173 |
0.389 |
YES |
0.435 |
-1.212 |
| Controle |
M |
|
|
|
fss.media.math |
22 |
3.794 |
3.764 |
3.111 |
4.667 |
0.369 |
0.079 |
0.164 |
0.417 |
NO |
0.589 |
0.089 |
| Experimental |
F |
|
|
|
fss.media.math |
7 |
3.825 |
3.556 |
3.333 |
4.556 |
0.500 |
0.189 |
0.463 |
0.778 |
YES |
0.303 |
-1.895 |
| Experimental |
M |
|
|
|
fss.media.math |
11 |
3.859 |
3.778 |
3.333 |
4.556 |
0.433 |
0.131 |
0.291 |
0.722 |
YES |
0.196 |
-1.626 |
| Controle |
|
Rural |
|
|
dfs.media.math |
54 |
3.274 |
3.111 |
2.333 |
4.444 |
0.553 |
0.075 |
0.151 |
0.719 |
YES |
0.404 |
-0.685 |
| Controle |
|
Urbana |
|
|
dfs.media.math |
17 |
3.222 |
3.222 |
2.333 |
4.000 |
0.480 |
0.116 |
0.247 |
0.667 |
YES |
-0.031 |
-1.067 |
| Experimental |
|
Rural |
|
|
dfs.media.math |
29 |
3.360 |
3.333 |
2.222 |
4.556 |
0.524 |
0.097 |
0.199 |
0.556 |
YES |
0.262 |
-0.189 |
| Experimental |
|
Urbana |
|
|
dfs.media.math |
8 |
2.819 |
2.667 |
1.889 |
4.111 |
0.732 |
0.259 |
0.612 |
0.694 |
YES |
0.434 |
-1.220 |
| Controle |
|
Rural |
|
|
fss.media.math |
54 |
3.406 |
3.444 |
2.444 |
4.667 |
0.528 |
0.072 |
0.144 |
0.556 |
YES |
0.139 |
-0.317 |
| Controle |
|
Urbana |
|
|
fss.media.math |
17 |
3.611 |
3.444 |
2.667 |
5.000 |
0.749 |
0.182 |
0.385 |
1.222 |
NO |
0.645 |
-1.076 |
| Experimental |
|
Rural |
|
|
fss.media.math |
29 |
3.280 |
3.333 |
2.000 |
4.556 |
0.653 |
0.121 |
0.248 |
0.778 |
YES |
0.093 |
-0.754 |
| Experimental |
|
Urbana |
|
|
fss.media.math |
8 |
3.184 |
2.889 |
2.444 |
4.333 |
0.735 |
0.260 |
0.615 |
0.840 |
NO |
0.610 |
-1.484 |
| Controle |
|
|
Parda |
|
dfs.media.math |
46 |
3.346 |
3.333 |
2.333 |
4.444 |
0.552 |
0.081 |
0.164 |
0.750 |
YES |
0.106 |
-0.877 |
| Controle |
|
|
Branca |
|
dfs.media.math |
9 |
3.395 |
3.333 |
2.889 |
4.444 |
0.553 |
0.184 |
0.425 |
0.444 |
NO |
0.749 |
-1.034 |
| Experimental |
|
|
Parda |
|
dfs.media.math |
12 |
3.315 |
3.333 |
2.444 |
4.222 |
0.540 |
0.156 |
0.343 |
0.750 |
YES |
0.097 |
-1.312 |
| Experimental |
|
|
Branca |
|
dfs.media.math |
6 |
3.093 |
3.333 |
2.222 |
3.667 |
0.568 |
0.232 |
0.596 |
0.667 |
NO |
-0.502 |
-1.742 |
| Controle |
|
|
Parda |
|
fss.media.math |
46 |
3.478 |
3.444 |
2.444 |
5.000 |
0.552 |
0.081 |
0.164 |
0.750 |
YES |
0.321 |
-0.031 |
| Controle |
|
|
Branca |
|
fss.media.math |
9 |
3.481 |
3.556 |
2.444 |
4.667 |
0.603 |
0.201 |
0.464 |
0.444 |
YES |
0.254 |
-0.347 |
| Experimental |
|
|
Parda |
|
fss.media.math |
12 |
3.259 |
3.278 |
2.111 |
4.333 |
0.589 |
0.170 |
0.374 |
0.722 |
YES |
-0.158 |
-0.653 |
| Experimental |
|
|
Branca |
|
fss.media.math |
6 |
3.319 |
3.333 |
2.667 |
4.250 |
0.577 |
0.236 |
0.606 |
0.639 |
YES |
0.332 |
-1.468 |
| Controle |
|
|
|
6 ano |
dfs.media.math |
32 |
3.232 |
3.167 |
2.333 |
4.444 |
0.510 |
0.090 |
0.184 |
0.583 |
NO |
0.542 |
-0.503 |
| Controle |
|
|
|
7 ano |
dfs.media.math |
31 |
3.336 |
3.333 |
2.444 |
4.222 |
0.482 |
0.087 |
0.177 |
0.646 |
YES |
-0.050 |
-0.883 |
| Controle |
|
|
|
8 ano |
dfs.media.math |
17 |
3.248 |
3.222 |
2.333 |
4.444 |
0.510 |
0.124 |
0.262 |
0.444 |
YES |
0.431 |
-0.031 |
| Controle |
|
|
|
9 ano |
dfs.media.math |
19 |
3.351 |
3.333 |
2.444 |
4.444 |
0.459 |
0.105 |
0.221 |
0.444 |
YES |
0.392 |
0.252 |
| Experimental |
|
|
|
6 ano |
dfs.media.math |
15 |
3.526 |
3.556 |
2.556 |
4.556 |
0.581 |
0.150 |
0.322 |
0.778 |
YES |
0.091 |
-1.193 |
| Experimental |
|
|
|
7 ano |
dfs.media.math |
11 |
3.303 |
3.333 |
2.556 |
4.222 |
0.533 |
0.161 |
0.358 |
0.833 |
YES |
0.059 |
-1.340 |
| Experimental |
|
|
|
8 ano |
dfs.media.math |
11 |
3.000 |
3.111 |
1.889 |
4.111 |
0.648 |
0.195 |
0.435 |
0.722 |
YES |
-0.138 |
-1.007 |
| Experimental |
|
|
|
9 ano |
dfs.media.math |
9 |
3.136 |
3.222 |
2.222 |
3.778 |
0.582 |
0.194 |
0.447 |
1.000 |
YES |
-0.316 |
-1.709 |
| Controle |
|
|
|
6 ano |
fss.media.math |
32 |
3.452 |
3.444 |
2.444 |
5.000 |
0.585 |
0.103 |
0.211 |
0.604 |
NO |
0.621 |
-0.007 |
| Controle |
|
|
|
7 ano |
fss.media.math |
31 |
3.480 |
3.444 |
2.444 |
5.000 |
0.582 |
0.104 |
0.213 |
0.778 |
YES |
0.463 |
0.060 |
| Controle |
|
|
|
8 ano |
fss.media.math |
17 |
3.291 |
3.111 |
2.444 |
4.556 |
0.551 |
0.134 |
0.283 |
0.667 |
NO |
0.597 |
-0.517 |
| Controle |
|
|
|
9 ano |
fss.media.math |
19 |
3.414 |
3.333 |
2.778 |
4.333 |
0.421 |
0.097 |
0.203 |
0.556 |
YES |
0.310 |
-0.807 |
| Experimental |
|
|
|
6 ano |
fss.media.math |
15 |
3.410 |
3.333 |
2.556 |
4.556 |
0.584 |
0.151 |
0.323 |
0.410 |
NO |
0.663 |
-0.723 |
| Experimental |
|
|
|
7 ano |
fss.media.math |
11 |
3.538 |
3.556 |
2.000 |
4.333 |
0.736 |
0.222 |
0.494 |
0.833 |
NO |
-0.733 |
-0.671 |
| Experimental |
|
|
|
8 ano |
fss.media.math |
11 |
2.960 |
2.778 |
2.222 |
4.556 |
0.680 |
0.205 |
0.457 |
0.722 |
NO |
1.074 |
0.066 |
| Experimental |
|
|
|
9 ano |
fss.media.math |
9 |
3.160 |
3.222 |
2.111 |
4.111 |
0.648 |
0.216 |
0.498 |
0.667 |
YES |
0.037 |
-1.341 |
ANCOVA Table Comparison
df <- do.call(plyr::rbind.fill, laov)
df <- df[!duplicated(df$Effect),]
| 1 |
dfs.media.math |
1 |
43 |
70.878 |
0.000 |
* |
0.622 |
1 |
142 |
36.730 |
0.000 |
* |
0.206 |
| 2 |
grupo |
1 |
43 |
4.567 |
0.038 |
* |
0.096 |
1 |
142 |
1.959 |
0.164 |
|
0.014 |
| 5 |
grupo:Sexo |
1 |
50 |
1.515 |
0.224 |
|
0.029 |
1 |
140 |
0.713 |
0.400 |
|
0.005 |
| 6 |
Sexo |
1 |
50 |
0.052 |
0.820 |
|
0.001 |
1 |
140 |
0.172 |
0.679 |
|
0.001 |
| 9 |
grupo:Zona |
1 |
103 |
0.024 |
0.878 |
|
0.000 |
1 |
103 |
0.024 |
0.878 |
|
0.000 |
| 10 |
Zona |
1 |
103 |
3.051 |
0.084 |
|
0.029 |
1 |
103 |
3.051 |
0.084 |
|
0.029 |
| 11 |
Cor.Raca |
1 |
68 |
0.073 |
0.787 |
|
0.001 |
1 |
68 |
0.073 |
0.787 |
|
0.001 |
| 14 |
grupo:Cor.Raca |
1 |
68 |
0.266 |
0.607 |
|
0.004 |
1 |
68 |
0.266 |
0.607 |
|
0.004 |
| 17 |
grupo:Serie |
3 |
136 |
0.486 |
0.693 |
|
0.011 |
3 |
136 |
0.486 |
0.693 |
|
0.011 |
| 18 |
Serie |
3 |
136 |
1.218 |
0.305 |
|
0.026 |
3 |
136 |
1.218 |
0.305 |
|
0.026 |
PairWise Table Comparison
df <- do.call(plyr::rbind.fill, lpwc)
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% c(names(lfatores),"term",".y.")])]
| Controle |
|
|
|
|
pre |
pos |
88 |
-5.554 |
0.000 |
0.000 |
**** |
286 |
-1.708 |
0.089 |
0.089 |
ns |
| Experimental |
|
|
|
|
pre |
pos |
88 |
-2.017 |
0.047 |
0.047 |
* |
286 |
-0.117 |
0.907 |
0.907 |
ns |
|
|
|
|
|
Controle |
Experimental |
43 |
2.137 |
0.038 |
0.038 |
* |
142 |
1.400 |
0.164 |
0.164 |
ns |
| Controle |
F |
|
|
|
pre |
pos |
102 |
-2.851 |
0.005 |
0.005 |
** |
282 |
-0.670 |
0.504 |
0.504 |
ns |
| Controle |
M |
|
|
|
pre |
pos |
102 |
-4.463 |
0.000 |
0.000 |
**** |
282 |
-1.706 |
0.089 |
0.089 |
ns |
| Controle |
|
|
|
|
F |
M |
50 |
-0.900 |
0.373 |
0.373 |
ns |
140 |
-0.809 |
0.420 |
0.420 |
ns |
| Experimental |
F |
|
|
|
pre |
pos |
102 |
-1.488 |
0.140 |
0.140 |
ns |
282 |
-0.901 |
0.368 |
0.368 |
ns |
| Experimental |
M |
|
|
|
pre |
pos |
102 |
-0.929 |
0.355 |
0.355 |
ns |
282 |
0.543 |
0.588 |
0.588 |
ns |
| Experimental |
|
|
|
|
F |
M |
50 |
0.886 |
0.380 |
0.380 |
ns |
140 |
0.484 |
0.629 |
0.629 |
ns |
|
F |
|
|
|
Controle |
Experimental |
50 |
0.687 |
0.495 |
0.495 |
ns |
140 |
0.224 |
0.823 |
0.823 |
ns |
|
M |
|
|
|
Controle |
Experimental |
50 |
2.674 |
0.010 |
0.010 |
* |
140 |
1.637 |
0.104 |
0.104 |
ns |
| Controle |
|
|
|
|
Rural |
Urbana |
103 |
-1.537 |
0.127 |
0.127 |
ns |
103 |
-1.537 |
0.127 |
0.127 |
ns |
| Controle |
|
Rural |
|
|
pre |
pos |
208 |
-1.169 |
0.244 |
0.244 |
ns |
208 |
-1.169 |
0.244 |
0.244 |
ns |
| Controle |
|
Urbana |
|
|
pre |
pos |
208 |
-1.943 |
0.053 |
0.053 |
ns |
208 |
-1.943 |
0.053 |
0.053 |
ns |
| Experimental |
|
|
|
|
Rural |
Urbana |
103 |
-0.856 |
0.394 |
0.394 |
ns |
103 |
-0.856 |
0.394 |
0.394 |
ns |
| Experimental |
|
Rural |
|
|
pre |
pos |
208 |
0.525 |
0.600 |
0.600 |
ns |
208 |
0.525 |
0.600 |
0.600 |
ns |
| Experimental |
|
Urbana |
|
|
pre |
pos |
208 |
-1.250 |
0.213 |
0.213 |
ns |
208 |
-1.250 |
0.213 |
0.213 |
ns |
|
|
Rural |
|
|
Controle |
Experimental |
103 |
1.364 |
0.175 |
0.175 |
ns |
103 |
1.364 |
0.175 |
0.175 |
ns |
|
|
Urbana |
|
|
Controle |
Experimental |
103 |
0.899 |
0.371 |
0.371 |
ns |
103 |
0.899 |
0.371 |
0.371 |
ns |
| Controle |
|
|
Branca |
|
pre |
pos |
138 |
-0.328 |
0.743 |
0.743 |
ns |
138 |
-0.328 |
0.743 |
0.743 |
ns |
| Controle |
|
|
|
|
Parda |
Branca |
68 |
0.085 |
0.932 |
0.932 |
ns |
68 |
0.085 |
0.932 |
0.932 |
ns |
| Controle |
|
|
Parda |
|
pre |
pos |
138 |
-1.127 |
0.262 |
0.262 |
ns |
138 |
-1.127 |
0.262 |
0.262 |
ns |
| Experimental |
|
|
Branca |
|
pre |
pos |
138 |
-0.703 |
0.483 |
0.483 |
ns |
138 |
-0.703 |
0.483 |
0.483 |
ns |
| Experimental |
|
|
|
|
Parda |
Branca |
68 |
-0.577 |
0.566 |
0.566 |
ns |
68 |
-0.577 |
0.566 |
0.566 |
ns |
| Experimental |
|
|
Parda |
|
pre |
pos |
138 |
0.243 |
0.808 |
0.808 |
ns |
138 |
0.243 |
0.808 |
0.808 |
ns |
|
|
|
Branca |
|
Controle |
Experimental |
68 |
0.136 |
0.892 |
0.892 |
ns |
68 |
0.136 |
0.892 |
0.892 |
ns |
|
|
|
Parda |
|
Controle |
Experimental |
68 |
1.212 |
0.230 |
0.230 |
ns |
68 |
1.212 |
0.230 |
0.230 |
ns |
| Controle |
|
|
|
6 ano |
pre |
pos |
274 |
-1.587 |
0.114 |
0.114 |
ns |
274 |
-1.587 |
0.114 |
0.114 |
ns |
| Controle |
|
|
|
7 ano |
pre |
pos |
274 |
-1.023 |
0.307 |
0.307 |
ns |
274 |
-1.023 |
0.307 |
0.307 |
ns |
| Controle |
|
|
|
8 ano |
pre |
pos |
274 |
-0.223 |
0.824 |
0.824 |
ns |
274 |
-0.223 |
0.824 |
0.824 |
ns |
| Controle |
|
|
|
9 ano |
pre |
pos |
274 |
-0.348 |
0.728 |
0.728 |
ns |
274 |
-0.348 |
0.728 |
0.728 |
ns |
| Controle |
|
|
|
|
6 ano |
7 ano |
136 |
0.172 |
0.864 |
1.000 |
ns |
136 |
0.172 |
0.864 |
1.000 |
ns |
| Controle |
|
|
|
|
6 ano |
8 ano |
136 |
1.067 |
0.288 |
1.000 |
ns |
136 |
1.067 |
0.288 |
1.000 |
ns |
| Controle |
|
|
|
|
6 ano |
9 ano |
136 |
0.630 |
0.530 |
1.000 |
ns |
136 |
0.630 |
0.530 |
1.000 |
ns |
| Controle |
|
|
|
|
7 ano |
8 ano |
136 |
0.916 |
0.361 |
1.000 |
ns |
136 |
0.916 |
0.361 |
1.000 |
ns |
| Controle |
|
|
|
|
7 ano |
9 ano |
136 |
0.479 |
0.633 |
1.000 |
ns |
136 |
0.479 |
0.633 |
1.000 |
ns |
| Controle |
|
|
|
|
8 ano |
9 ano |
136 |
-0.410 |
0.682 |
1.000 |
ns |
136 |
-0.410 |
0.682 |
1.000 |
ns |
| Experimental |
|
|
|
6 ano |
pre |
pos |
274 |
0.571 |
0.569 |
0.569 |
ns |
274 |
0.571 |
0.569 |
0.569 |
ns |
| Experimental |
|
|
|
7 ano |
pre |
pos |
274 |
-0.992 |
0.322 |
0.322 |
ns |
274 |
-0.992 |
0.322 |
0.322 |
ns |
| Experimental |
|
|
|
8 ano |
pre |
pos |
274 |
0.171 |
0.865 |
0.865 |
ns |
274 |
0.171 |
0.865 |
0.865 |
ns |
| Experimental |
|
|
|
9 ano |
pre |
pos |
274 |
-0.094 |
0.925 |
0.925 |
ns |
274 |
-0.094 |
0.925 |
0.925 |
ns |
| Experimental |
|
|
|
|
6 ano |
7 ano |
136 |
-1.123 |
0.263 |
1.000 |
ns |
136 |
-1.123 |
0.263 |
1.000 |
ns |
| Experimental |
|
|
|
|
6 ano |
8 ano |
136 |
0.900 |
0.370 |
1.000 |
ns |
136 |
0.900 |
0.370 |
1.000 |
ns |
| Experimental |
|
|
|
|
6 ano |
9 ano |
136 |
0.261 |
0.795 |
1.000 |
ns |
136 |
0.261 |
0.795 |
1.000 |
ns |
| Experimental |
|
|
|
|
7 ano |
8 ano |
136 |
1.894 |
0.060 |
0.362 |
ns |
136 |
1.894 |
0.060 |
0.362 |
ns |
| Experimental |
|
|
|
|
7 ano |
9 ano |
136 |
1.241 |
0.217 |
1.000 |
ns |
136 |
1.241 |
0.217 |
1.000 |
ns |
| Experimental |
|
|
|
|
8 ano |
9 ano |
136 |
-0.565 |
0.573 |
1.000 |
ns |
136 |
-0.565 |
0.573 |
1.000 |
ns |
|
|
|
|
6 ano |
Controle |
Experimental |
136 |
1.110 |
0.269 |
0.269 |
ns |
136 |
1.110 |
0.269 |
0.269 |
ns |
|
|
|
|
7 ano |
Controle |
Experimental |
136 |
-0.398 |
0.691 |
0.691 |
ns |
136 |
-0.398 |
0.691 |
0.691 |
ns |
|
|
|
|
8 ano |
Controle |
Experimental |
136 |
1.019 |
0.310 |
0.310 |
ns |
136 |
1.019 |
0.310 |
0.310 |
ns |
|
|
|
|
9 ano |
Controle |
Experimental |
136 |
0.688 |
0.493 |
0.493 |
ns |
136 |
0.688 |
0.493 |
0.493 |
ns |
EMMS Table Comparison
df <- do.call(plyr::rbind.fill, lemms)
df[["N-N'"]] <- df[["N"]] - df[["N'"]]
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% names(lfatores)])]
| Controle |
|
|
|
|
30 |
3.259 |
0.095 |
3.916 |
0.061 |
3.986 |
0.042 |
3.900 |
4.071 |
99 |
3.290 |
0.049 |
3.426 |
0.055 |
3.423 |
0.053 |
3.318 |
3.527 |
-69 |
| Experimental |
|
|
|
|
16 |
3.632 |
0.140 |
3.958 |
0.105 |
3.827 |
0.059 |
3.708 |
3.946 |
46 |
3.271 |
0.089 |
3.284 |
0.099 |
3.291 |
0.078 |
3.137 |
3.445 |
-30 |
| Controle |
F |
|
|
|
15 |
3.356 |
0.117 |
3.833 |
0.081 |
3.837 |
0.058 |
3.720 |
3.954 |
45 |
3.310 |
0.069 |
3.389 |
0.076 |
3.375 |
0.079 |
3.219 |
3.531 |
-30 |
| Controle |
M |
|
|
|
22 |
3.177 |
0.116 |
3.794 |
0.079 |
3.906 |
0.049 |
3.806 |
4.005 |
54 |
3.273 |
0.069 |
3.456 |
0.079 |
3.462 |
0.072 |
3.319 |
3.604 |
-32 |
| Experimental |
F |
|
|
|
7 |
3.460 |
0.263 |
3.825 |
0.189 |
3.766 |
0.086 |
3.594 |
3.938 |
17 |
3.046 |
0.167 |
3.218 |
0.188 |
3.341 |
0.130 |
3.084 |
3.598 |
-10 |
| Experimental |
M |
|
|
|
11 |
3.677 |
0.129 |
3.859 |
0.131 |
3.668 |
0.071 |
3.526 |
3.811 |
29 |
3.402 |
0.096 |
3.323 |
0.115 |
3.262 |
0.099 |
3.066 |
3.457 |
-18 |
| Controle |
|
Rural |
|
|
54 |
3.274 |
0.075 |
3.406 |
0.072 |
3.396 |
0.074 |
3.249 |
3.543 |
54 |
3.274 |
0.075 |
3.406 |
0.072 |
3.396 |
0.074 |
3.249 |
3.543 |
0 |
| Controle |
|
Urbana |
|
|
17 |
3.222 |
0.116 |
3.611 |
0.182 |
3.629 |
0.132 |
3.367 |
3.891 |
17 |
3.222 |
0.116 |
3.611 |
0.182 |
3.629 |
0.132 |
3.367 |
3.891 |
0 |
| Experimental |
|
Rural |
|
|
29 |
3.360 |
0.097 |
3.280 |
0.121 |
3.224 |
0.102 |
3.022 |
3.426 |
29 |
3.360 |
0.097 |
3.280 |
0.121 |
3.224 |
0.102 |
3.022 |
3.426 |
0 |
| Experimental |
|
Urbana |
|
|
8 |
2.819 |
0.259 |
3.184 |
0.260 |
3.416 |
0.197 |
3.024 |
3.807 |
8 |
2.819 |
0.259 |
3.184 |
0.260 |
3.416 |
0.197 |
3.024 |
3.807 |
0 |
| Controle |
|
|
Branca |
|
9 |
3.395 |
0.184 |
3.481 |
0.201 |
3.453 |
0.174 |
3.105 |
3.801 |
9 |
3.395 |
0.184 |
3.481 |
0.201 |
3.453 |
0.174 |
3.105 |
3.801 |
0 |
| Controle |
|
|
Parda |
|
46 |
3.346 |
0.081 |
3.478 |
0.081 |
3.469 |
0.077 |
3.316 |
3.623 |
46 |
3.346 |
0.081 |
3.478 |
0.081 |
3.469 |
0.077 |
3.316 |
3.623 |
0 |
| Experimental |
|
|
Branca |
|
6 |
3.093 |
0.232 |
3.319 |
0.236 |
3.415 |
0.215 |
2.986 |
3.845 |
6 |
3.093 |
0.232 |
3.319 |
0.236 |
3.415 |
0.215 |
2.986 |
3.845 |
0 |
| Experimental |
|
|
Parda |
|
12 |
3.315 |
0.156 |
3.259 |
0.170 |
3.264 |
0.151 |
2.963 |
3.565 |
12 |
3.315 |
0.156 |
3.259 |
0.170 |
3.264 |
0.151 |
2.963 |
3.565 |
0 |
| Controle |
|
|
|
6 ano |
32 |
3.232 |
0.090 |
3.452 |
0.103 |
3.477 |
0.094 |
3.292 |
3.663 |
32 |
3.232 |
0.090 |
3.452 |
0.103 |
3.477 |
0.094 |
3.292 |
3.663 |
0 |
| Controle |
|
|
|
7 ano |
31 |
3.336 |
0.087 |
3.480 |
0.104 |
3.455 |
0.095 |
3.266 |
3.643 |
31 |
3.336 |
0.087 |
3.480 |
0.104 |
3.455 |
0.095 |
3.266 |
3.643 |
0 |
| Controle |
|
|
|
8 ano |
17 |
3.248 |
0.124 |
3.291 |
0.134 |
3.308 |
0.128 |
3.054 |
3.562 |
17 |
3.248 |
0.124 |
3.291 |
0.134 |
3.308 |
0.128 |
3.054 |
3.562 |
0 |
| Controle |
|
|
|
9 ano |
19 |
3.351 |
0.105 |
3.414 |
0.097 |
3.381 |
0.121 |
3.141 |
3.621 |
19 |
3.351 |
0.105 |
3.414 |
0.097 |
3.381 |
0.121 |
3.141 |
3.621 |
0 |
| Experimental |
|
|
|
6 ano |
15 |
3.526 |
0.150 |
3.410 |
0.151 |
3.292 |
0.138 |
3.019 |
3.565 |
15 |
3.526 |
0.150 |
3.410 |
0.151 |
3.292 |
0.138 |
3.019 |
3.565 |
0 |
| Experimental |
|
|
|
7 ano |
11 |
3.303 |
0.161 |
3.538 |
0.222 |
3.528 |
0.159 |
3.213 |
3.844 |
11 |
3.303 |
0.161 |
3.538 |
0.222 |
3.528 |
0.159 |
3.213 |
3.844 |
0 |
| Experimental |
|
|
|
8 ano |
11 |
3.000 |
0.195 |
2.960 |
0.205 |
3.098 |
0.161 |
2.779 |
3.417 |
11 |
3.000 |
0.195 |
2.960 |
0.205 |
3.098 |
0.161 |
2.779 |
3.417 |
0 |
| Experimental |
|
|
|
9 ano |
9 |
3.136 |
0.194 |
3.160 |
0.216 |
3.233 |
0.177 |
2.883 |
3.582 |
9 |
3.136 |
0.194 |
3.160 |
0.216 |
3.233 |
0.177 |
2.883 |
3.582 |
0 |